Our work also has limitations. First, we evaluated only a single diagnostic use case of ICH detection on CT scans of the head, albeit with multiple datasets from different clinical populations. However, our approach is applicable to any other medical imaging use case that utilizes cross-sectional imaging, including diagnosis of disease on CT of other body parts, as well as on other imaging modalities, such as MRI. Future studies will apply our approach to other use cases to validate its generalizability in other diagnostic scenarios and imaging modalities. Second, while this study demonstrated that indeed the examination-level annotations suffice for ICH detection in CT once enough training data is available, some image-level annotations were needed to validate our methodology. In future extensions to other diagnostic tasks or imaging modalities, this minimal amount of locally annotated data will also be necessary for validation purposes. This number of local annotations is very small, however: in this work we employed 1,000 examinations of the validation split of the RSNA dataset to this end, requiring about \(35 \times 10^3\)  image-level labels. This represents less than 6% of the number of image-level labels needed to train an alternative strongly supervised model. Third, the weakly supervised method currently only evaluates medical imaging data; given the potential improvements in imaging diagnoses using multimodal AI models [1]} incorporating multiple types of medical data (e.g., imaging, clinical symptoms, laboratory values), developing weakly supervised DL models that can incorporate multiple data types is an important topic for future study. Finally, although Convolutional Neural Networks (CNNs)—such as the models used in this work—remain the most popular deep learning architecture in medical imaging, it remains important to investigate whether these results extend to other parametrization of the predictors and architecture choices, for example to Vision Transformers (ViTs) [2]}, [3]}—which are rapidly gaining popularity in the field.
